IAFMNet：面向高效超分辨率的信息感知特征调制
IAFMNet: Information-Aware Feature Modulation for Efficient Super-Resolution
用无监督估计的信息密度图引导稀疏卷积与仿射调制，把计算动态集中到纹理/边缘区域，为高效超分及其他密集预测任务提供可复用的「按信息量分配算力」范式。
原文依据 a sparse convolution branch that dynamically allocates computation to informative areas while bypassing low-information regions
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论单图像超分辨率（SISR）旨在从低分辨率（LR）输入重建高分辨率（HR）图像，在真实世界的计算约束下这一任务愈发具有挑战性。然而，大多数高效 SISR 方法采用轻量、空间均匀的策略，在所有区域分配相同的计算量与注意力，忽略了视觉复杂度分布的不均匀性。从信息论角度看，纹理与边缘本身携带更关键的信息，导致重建误差不成比例地集中于这些区域。这促使我们将更多计算资源与注意力分配给这些信息丰富的区域。本文提出 IAFMNet，一种用于高效超分辨率的信息感知特征调制网络。其核心是信息密度图（IDM），通过最小化信息熵损失以无监督方式估计得到，从而突出估计编码代价较高的信息丰富区域。在 IDM 的引导下，IAFMNet 采用协同的双分支设计：（1）稀疏卷积分支，将计算动态分配给信息丰富区域，同时跳过低信息区域；（2）隐式调制分支，通过信息感知的仿射变换自适应地强调复杂区域。大量实验表明，IAFMNet 能有效识别信息丰富区域，并在降低计算开销的同时取得更优的视觉保真度。
英文原文摘要 1537 字符
Single Image Super-Resolution (SISR) aims to reconstruct a high-resolution (HR) image from a low-resolution (LR) input, a task that becomes increasingly challenging under real-world computational constraints. However, most efficient SISR methods adopt lightweight, spatially uniform strategies that allocate equal computation and attention across all regions, ignoring the uneven distribution of visual complexity. From an information-theoretic perspective, textures and edges inherently carry more critical information, resulting in reconstruction errors that are disproportionately concentrated in these regions. This motivates the allocation of greater computational resources and attention to these informative areas. In this paper, we propose IAFMNet, an Information-Aware Feature Modulation network for efficient SR. At its core lies the Information Density Map (IDM), which is estimated in an unsupervised manner by minimizing the Information Entropy Loss, thereby highlighting informative regions with high estimated encoding costs. Guided by the IDM, IAFMNet adopts a synergistic dual-branch design: (1) a sparse convolution branch that dynamically allocates computation to informative areas while bypassing low-information regions, and (2) an implicit modulation branch that adaptively emphasizes complex regions through information-aware affine transformations. Extensive experiments demonstrate that IAFMNet effectively identifies informative regions and achieves superior visual fidelity with reduced computational overhead.